88 research outputs found
Closed-Loop Tuning of Cascade Controller for Load Frequency Control of Multi-Area Distributed Generation Resources Optimized by ASOS Algorithm
This paper provides closed loop tuning of cascaded-tilted integral derivative controller (CC-TID) for load frequency control (LFC) of micro grid system. A micro grid system is the arrangement of distributed generation resources such as wind turbine generator (WTG), fuel cell (FC), aqua electrolyser (AE), diesel engine generator (DEG) and battery energy storage system (BESS). Different controllers such as proportional integral derivative (PID), two degree of freedom (2DOFPID), three degree of freedom (3DOFPID) and tilted integral derivative (TID) are used not only to sustain the disparity between real power generation and load demand but also accomplish zero steady state error to enrich the frequency and tie power regulations. The anticipated controller encompasses both the value of cascade (CC) and fractional order (FO) controls for better elimination of system instabilities. In the proposed CC-3DOFPID-TID controller, TID controller is castoff as a slave controller and 3DOFPID controller aided the role of dominant controller. The controlled parameters are optimized by adaptive symbiotic organism search (ASOS) algorithm for keen results of difficulties in LFC. To persist in ecosystem, symbiotic relations are predictable by organism through imitators. Further the dynamic behaviours of controller optimized by ASOS, teaching learning based optimization (TLBO) and differential evolution particle swarm optimization (DEPSO) are compared by extensive simulations in MATLAB/SIMULINK. Moreover the supremacy of proposed controller is performed through system dynamics comparison among PID, 2DOFPID, 3DOF-PID and CC-3DOFPID-TID controllers. Finally sensitivity of proposed controller has proven though random load perturbation
A Comparative Performance Assessment of Evolutionary Fractional Order PID Controllers for Magnetic Levitation Plant with Time Delay
322-327Fractional Order controllers have been extensively applied to various fields of science and engineering, since several decades, because of the ability to control more parameters and consequent better control. However, to achieve this advantage, proper tuning of the associated parameters plays an important role. To achieve this objective, this paper employs a multi-agent symbiotic organisms search (MASOS) algorithm for appropriately tuning the parameters of fractional order proportional-integral-derivative (FOPID) controller for stabilizing a magnetic levitation plant (MLP) with time delay. Three different FOPID controllers have been precisely tuned and their performance has been evaluated and compared in this paper. The results demonstrate that the I-PD configuration produces the best performance in terms of time domain as well as frequency domain specifications, when compared with the other configurations
A Comparative Performance Assessment of Evolutionary Fractional Order PID Controllers for Magnetic Levitation Plant with Time Delay
Fractional Order controllers have been extensively applied to various fields of science and engineering, since several decades, because of the ability to control more parameters and consequent better control. However, to achieve this advantage, proper tuning of the associated parameters plays an important role. To achieve this objective, this paper employs a multi-agent symbiotic organisms search (MASOS) algorithm for appropriately tuning the parameters of fractional order proportional-integral-derivative (FOPID) controller for stabilizing a magnetic levitation plant (MLP) with time delay. Three different FOPID controllers have been precisely tuned and their performance has been evaluated and compared in this paper. The results demonstrate that the I-PD configuration produces the best performance in terms of time domain as well as frequency domain specifications, when compared with the other configurations
Chaotic-based particle swarm optimization algorithm for optimal PID tuning in automatic voltage regulator systems
Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.Вступ. В електроенергетичній системі потужність синхронних генераторів змінюється внаслідок збурень або різких змін навантаження. Ці зміни в потужності серйозно впливають на стабільність енергетичної системи та якість електроенергії. Синхронний генератор оснащений автоматичним регулятором напруги для підтримання напруги на його клемах на рівні номінальної напруги. Декілька методів управління використовуються для поліпшення реакції системи автоматичного регулятора напруги, однак пропорційний інтегральний похідний контролер (PID-контролер) є найбільш часто використовуваним контролером, але його параметри вимагають оптимізації. Новизна. У цій роботі хаотична послідовність, заснована на логістичній схемі, гібридизується за допомогою оптимізації рою частинок, щоб знайти оптимальні параметри PID для системи автоматичного регулятора напруги. Ініціалізація на основі хаотичної послідовності логістичної схеми та найкращий глобальний вибір дозволяють алгоритму вийти із локальної мінімальної стагнації та покращити швидкість збіжності, що дає найкращі оптимальні параметри. Мета. Основною метою запропонованого підходу є поліпшення перехідної реакції системи автоматичного регулятора напруги шляхом мінімізації максимального перевищення, часу встановлення, часу наростання та пікових значень напруги на клемах і усунення помилки у стаціонарного стані. Методи. У процесі настройки параметрів техніку оптимізації рою хаотичних частинок кілька разів пропускали через запропоновану гібридну цільову функцію, яка враховує переваги двох найбільш часто використовуваних цільових функцій з мінімальною кількістю ітерацій,і знайдено оптимальне значення коефіцієнту підсилення PID. Запропонований алгоритм порівнюється з сучасними метаевристичними алгоритмами, включаючи звичайну оптимізацію рою частинок, вдосконалений алгоритм нирок та інші. Результати. Для оцінки ефективності об'єднуються характеристики інтеграла у часі, помноженого на похибки у квадраті, та цільових функцій Цве-Лі Гейнга. Крім того, проводяться аналіз у часовій області, аналіз у частотної області та аналіз стійкості, щоб показати кращу ефективність запропонованого алгоритму. Результат показує, що автоматичний регулятор напруги, налаштований на хаотичну оптимізацію рою частинок, заснований на поліпшенні виходу PID в перевищеннях,часі налаштування та значенні функції перевищує на 14,41 %, 37,91 %, 1,73 % нещодавно запропонований нирковий алгоритм та на 43,55 %, 44,5 %, 16,67 % перевищує звичайні алгоритми оптимізації рою частинок. Поліпшення перехідної реакції ще більше покращує стабільність автоматичного регулятора напруги для систем електроенергетики
Chaotic-based particle swarm optimization algorithm for optimal PID tuning in automatic voltage regulator systems
Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.Вступ. В електроенергетичній системі потужність синхронних генераторів змінюється внаслідок збурень або різких змін навантаження. Ці зміни в потужності серйозно впливають на стабільність енергетичної системи та якість електроенергії. Синхронний генератор оснащений автоматичним регулятором напруги для підтримання напруги на його клемах на рівні номінальної напруги. Декілька методів управління використовуються для поліпшення реакції системи автоматичного регулятора напруги, однак пропорційний інтегральний похідний контролер (PID-контролер) є найбільш часто використовуваним контролером, але його параметри вимагають оптимізації. Новизна. У цій роботі хаотична послідовність, заснована на логістичній схемі, гібридизується за допомогою оптимізації рою частинок, щоб знайти оптимальні параметри PID для системи автоматичного регулятора напруги. Ініціалізація на основі хаотичної послідовності логістичної схеми та найкращий глобальний вибір дозволяють алгоритму вийти із локальної мінімальної стагнації та покращити швидкість збіжності, що дає найкращі оптимальні параметри. Мета. Основною метою запропонованого підходу є поліпшення перехідної реакції системи автоматичного регулятора напруги шляхом мінімізації максимального перевищення, часу встановлення, часу наростання та пікових значень напруги на клемах і усунення помилки у стаціонарного стані. Методи. У процесі настройки параметрів техніку оптимізації рою хаотичних частинок кілька разів пропускали через запропоновану гібридну цільову функцію, яка враховує переваги двох найбільш часто використовуваних цільових функцій з мінімальною кількістю ітерацій,і знайдено оптимальне значення коефіцієнту підсилення PID. Запропонований алгоритм порівнюється з сучасними метаевристичними алгоритмами, включаючи звичайну оптимізацію рою частинок, вдосконалений алгоритм нирок та інші. Результати. Для оцінки ефективності об'єднуються характеристики інтеграла у часі, помноженого на похибки у квадраті, та цільових функцій Цве-Лі Гейнга. Крім того, проводяться аналіз у часовій області, аналіз у частотної області та аналіз стійкості, щоб показати кращу ефективність запропонованого алгоритму. Результат показує, що автоматичний регулятор напруги, налаштований на хаотичну оптимізацію рою частинок, заснований на поліпшенні виходу PID в перевищеннях,часі налаштування та значенні функції перевищує на 14,41 %, 37,91 %, 1,73 % нещодавно запропонований нирковий алгоритм та на 43,55 %, 44,5 %, 16,67 % перевищує звичайні алгоритми оптимізації рою частинок. Поліпшення перехідної реакції ще більше покращує стабільність автоматичного регулятора напруги для систем електроенергетики
Advances in Optimization and Nonlinear Analysis
The present book focuses on that part of calculus of variations, optimization, nonlinear analysis and related applications which combines tools and methods from partial differential equations with geometrical techniques. More precisely, this work is devoted to nonlinear problems coming from different areas, with particular reference to those introducing new techniques capable of solving a wide range of problems. The book is a valuable guide for researchers, engineers and students in the field of mathematics, operations research, optimal control science, artificial intelligence, management science and economics
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Intelligent Decision Support Systems in Ventilation Management
Introduction: Intensive Care Unit (ICU) medical personnel, in an ongoing process termed ventilation management, utilize patient physiology and pathology data to define ventilator apparatus settings. Aims: The aim of the research is to develop and evaluate in comparison hybrid ventilation advisor systems, that could support ventilation management process, specific to lung pathology for patients ventilated in control mode. Methodology: A questionnaire was designed and circulated to Intensivists. Patient data, as defined by the questionnaire analysis, were collected and categorized into three lung pathologies. Three ICU doctors evaluated correlation analysis of the recorded data. Evaluation results were used for identifying models basic architecture. Two custom software toolboxes were developed for developing hybrid systems; namely the EVolution Of Fuzzy INference Engines (EVOFINE) and the FUzzy Neural (FUN) toolbox. Eight hybrid systems developed with EVOFINE, FUN, ANFIS and ANN techniques were evaluated against applied clinical decisions and patient scenarios. Results: Seventeen (17) models were designed for each of the eight (8) modeling techniques. The modelled process consisted of twelve physiology variables and six ventilator settings. The number of models’ inputs ranged from single to six based on correlation and evaluation findings. Evaluation against clinical recommendations has shown that ANNs performed better; mean average error as percentage for four of the applied techniques was 0.16%, 1.29% & 0.62 for ANN empirical, 0.05%, 2.23% & 2.30% for ANFIS, 0.93%, 2.33% & 1.89% for EVOFINE and 0.73%, 2.63% & 6.56 for FUN NM, in Normal, COPD and ALI-ARDS categories respectively. Additionally evaluation against clinical disagreement SD has shown that 70.6% of the NN empirical models were performing in 90% of their suggestions within clinical SD, while the percentages were 53%, 53% and 59% for the EVOFINE, ANFIS and NN Normalized models respectively. The EVOFINE and ANFIS produced Fuzzy Systems whose architecture is transparent for the user. Visual observation of ANFIS architectures revealed possibly hazardous advices. Evaluation against clinical disagreement has shown that the NN empirical was not producing hazardous advices, while EVOFINE, ANFIS and NN Normalized were shown to produce potentially hazardous advice in 17.6%, 23% and 5.8% of the developed models
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Modelling and design of the eco-system of causality for real-time systems
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The purpose of this research work is to propose an improved method for real-time sensitivity analysis (SA) applicable to large-scale complex systems. Borrowed from the EventTracker principle of the interrelation of causal events, it deploys the Rank Order Clustering (ROC) method to automatically group every relevant system input to parameters that represent the system state (i.e. output). The fundamental principle of event modelling is that the state of a given system is a function of every acquirable piece of knowledge or data (input) of events that occur within the system and its wider operational environment unless proven otherwise. It therefore strives to build the theoretical and practical foundation for the engineering of input data. The event modelling platform proposed attempts to filter unwanted data, and more importantly, include information that was thought to be irrelevant at the outset of the design process. The underpinning logic of the proposed Event Clustering technique (EventiC) is to build causal relationship between the events that trigger the inputs and outputs of the system. EventiC groups inputs with relevant corresponding outputs and measures the impact of each input variable on the output variables in short spans of time (relative real-time). It is believed that this grouping of relevant input-output event data by order of its importance in real-time is the key contribution to knowledge in this subject area. Our motivation is that components of current complex and organised systems are capable of generating and sharing information within their network of interrelated devices and systems. In addition to being an intelligent recorder of events, EventiC could also be a platform for preliminary data and knowledge construction. This improvement in the quality, and at times the quantity of input data, may lead to improved higher level mathematical formalism. It is hoped that better models will translate into superior controls and decision making. It is therefore believed that the projected outcome of this research work can be used to predict, stabilize (control), and optimize (operational research) the work of complex systems in the shortest possible time. For proof of concept, EventiC was designed using the MATLAB package and implemented using real-time data from the monitoring and control system of a typical cement manufacturing plant. The purpose for this deployment was to test and validate the concept, and to demonstrate whether the clusters of input data and their levels of importance against system performance indicators could be approved by industry experts. EventiC was used as an input variable selection tool for improving the existing fuzzy controller of the plant. Finally, EventiC was compared with its predecessor EventTracker using the same case study. The results revealed improvements in both computational efficiency and the quality of input variable selection
Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles
Full version unavailable due to 3rd party copyright restrictions.The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs).
Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller.
Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies.
The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV.
It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.EOARD (European Office of Aerospace Research & Development), euCognitio
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